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            <a href="/2019/05/20/user-17163699-1558319415/Python/预测-/">
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                <span class="date">2019-05-20</span>
            
            
            
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        <h1 class="title">预测:</h1>
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          <h1>第一步先主成分分析，</h1><h3>1找出主factor  下降维度<br><b>2<a href="https://www.jianshu.com/p/f2e331e5ec1f" target="_blank">特征工程</a>，<a href="https://www.jianshu.com/p/2cfbb5f8b195" target="_blank">特征工程2</a>增加纬度</b>
</h3><hr id="null"><hr id="null"><p><br></p><h1>第二步分情况:</h1><hr id="null"><h1>例一:<a href="https://blog.csdn.net/qq_33186949/article/details/80558766" target="_blank">房价预测</a>;<a href="https://www.kaggle.com/marsggbo/kaggle" target="_blank">2</a><br>
</h1><h4>（1）先聚类(无监督)，</h4><h4>（2）再拟合<br>1各种映射最小二乘<br> 2增加条目0 1，对此进行线性回归 回到原函数映射<a href="https://www.jianshu.com/p/44e96cdff91d" target="_blank">季节性+趋势</a><br>3各种映射放缩组合boosting:<a href="https://baijiahao.baidu.com/s?id=1595285625542671386&amp;wfr=spider&amp;for=pc" target="_blank">boosting 算法 CatBoost、Light GBM 和 XGBoost 进行了对比；虽然本文结论依据于特定的数据集，但通常情况下，XGBoost 都比另外两个算法慢。</a><a href="https://blog.csdn.net/legendavid/article/details/78904353" target="_blank">GBDT</a>（也属于boosting）</h4><hr id="null"><h1>例二:泰坦尼克号是否成活<a href="https://blog.csdn.net/error404404/article/details/81669929" target="_blank">1</a>，<a href="https://mp.weixin.qq.com/s?__biz=MzU1Nzg0OTQ1Mg==&amp;mid=2247484623&amp;idx=1&amp;sn=54517a5b168fb430d9ed06797ef62d66&amp;chksm=fc2ecacccb5943da48dada307de3b72710bf999fb97d55db07c0bdb7c59ef0b7890a881c99dd&amp;xtrack=1&amp;scene=0&amp;subscene=131&amp;clicktime=1556798439&amp;ascene=7&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABQAmlx4AzJkeANyZHgDumR4A8ZkeAAAA&amp;lang=zh_CN&amp;pass_ticket=1%2BzuzmQrbWlziYoTcWjEnzWACRKxtTdQj5ZDnNOIIEZNHUVdJEmABsePMy5PA9Vl&amp;wx_header=1" target="_blank">2</a>
</h1><h1>   西瓜是否熟了<br>  是否患有糖尿病</h1><h1><a href="https://mp.weixin.qq.com/s?__biz=MzA4MTk3ODI2OA==&amp;mid=2650341603&amp;idx=1&amp;sn=6766f78d4820a1d8fba5be710e219b1d&amp;chksm=87811610b0f69f06065f1c3c91ea92a42c769b6b1907adfa2a602db4e91315abb54cb8b6d6a7&amp;mpshare=1&amp;scene=2&amp;srcid=0503YoSaVTAJVe1HWwPIaJpL&amp;from=timeline&amp;ascene=2&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABQAmlx4AzJkeANyZHgDumR4A8ZkeAAAA&amp;lang=zh_CN&amp;pass_ticket=7p7iQ2C3RXR8cHY8blg15EwvkB5Ti4QuDHbJgSSHopTw1J187ZFiDArrpTUIcmWm&amp;wx_header=1" target="_blank">文本分类cnn rnn实现</a></h1><p>分类（有监督）</p><p>即基于一组预测变量预测一个分类结果（二分类）。<a href="https://www.jianshu.com/p/0f3bf1aeb8a3" target="_blank">有监督机器学习领域中包含可用于分类的方法：</a>逻辑回归、KNN（k临近）、决策树、随机森林、支持向量机（svm）、神经网络等。</p><hr id="null"><h1>例三 <a href="https://www.jianshu.com/p/73adce3fd975" target="_blank">GBDT+LR CTR预估-Kaggle实例[有数据集]</a>
</h1><p><a href="http://www.meihua.info/a/72378" target="_blank">点击率（ctr）解释</a></p><hr id="null"><hr id="null"><h1><b>第三步，<a href="https://blog.csdn.net/LoveL_T/article/details/84066419" target="_blank">正则化（防止过拟合）</a></b></h1><hr id="null"><p>最后:预测结果<a href="https://www.jianshu.com/p/2ad360edd219" target="_blank">常用评分标准</a></p><p>If you are interesting in this topic.﻿<br>You can get in touch with me.﻿<br>18234056952(Tel  wechat  qq)</p>
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